東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Approximation of Euclidean metric by...

Mukhopadhyay, Jayanta.

Linked to FindBook

Google Book

Amazon

博客來

Approximation of Euclidean metric by digital distances

Record Type:	Electronic resources : Monograph/item
Title/Author:	Approximation of Euclidean metric by digital distances/ by Jayanta Mukhopadhyay.
Author:	Mukhopadhyay, Jayanta.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xx, 144 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Geometry, Space and Metrics -- Digital distances: Classes and hierarchies -- Error analysis analytical approaches -- Linear combination of digital distances.
Contained By:	Springer Nature eBook
Subject:	Distance geometry. -
Online resource:	https://doi.org/10.1007/978-981-15-9901-9
ISBN:	9789811599019

Approximation of Euclidean metric by digital distances
Mukhopadhyay, Jayanta.

Approximation of Euclidean metric by digital distances[electronic resource] /by Jayanta Mukhopadhyay. - Singapore :Springer Singapore :2020. - xx, 144 p. :ill. (some col.), digital ;24 cm.

Geometry, Space and Metrics -- Digital distances: Classes and hierarchies -- Error analysis analytical approaches -- Linear combination of digital distances.

This book discusses different types of distance functions defined in an n-D integral space for their usefulness in approximating the Euclidean metric. It discusses the properties of these distance functions and presents various kinds of error analysis in approximating Euclidean metrics. It also presents a historical perspective on efforts and motivation for approximating Euclidean metrics by digital distances from the mid-sixties of the previous century. The book also contains an in-depth presentation of recent progress, and new research problems in this area.

ISBN: 9789811599019

Standard No.: 10.1007/978-981-15-9901-9doiSubjects--Topical Terms:

952297
Distance geometry.

LC Class. No.: QA613

Dewey Class. No.: 514.3

Approximation of Euclidean metric by digital distances
LDR:01730nmm a2200337 a 4500 001 2257454
003 DE-He213
005 20201202120754.0
006 m d
007 cr nn 008maaau
008 220420s2020 si s 0 eng d
020 $a 9789811599019 $q (electronic bk.)
020 $a 9789811599002 $q (paper)
024 7 $a 10.1007/978-981-15-9901-9 $2 doi
035 $a 978-981-15-9901-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA613
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 514.3 $2 23
090 $a QA613 $b .M953 2020
100 1 $a Mukhopadhyay, Jayanta. $3 2058772
245 1 0 $a Approximation of Euclidean metric by digital distances $h [electronic resource] / $c by Jayanta Mukhopadhyay.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xx, 144 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Geometry, Space and Metrics -- Digital distances: Classes and hierarchies -- Error analysis analytical approaches -- Linear combination of digital distances.
520 $a This book discusses different types of distance functions defined in an n-D integral space for their usefulness in approximating the Euclidean metric. It discusses the properties of these distance functions and presents various kinds of error analysis in approximating Euclidean metrics. It also presents a historical perspective on efforts and motivation for approximating Euclidean metrics by digital distances from the mid-sixties of the previous century. The book also contains an in-depth presentation of recent progress, and new research problems in this area.
650 0 $a Distance geometry. $3 952297
650 1 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Functional Analysis. $3 893943
650 2 4 $a Discrete Mathematics in Computer Science. $3 892513
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-15-9901-9
950 $a Computer Science (SpringerNature-11645)